Risk-based comprehension intervention for important documents

ABSTRACT

In providing visual cues in a document, a server captures search term(s) input by a user and detects a user selection of a document returned based on the search term(s). The server determines a dataset describing the user, where the dataset includes the search term(s), the document, and a set of demographic data of the user. The server maps the dataset to a user group, where the user group is associated with an expertise level for a domain associated with the document. The server assigns the expertise level associated with the user group to the user and maps the expertise level of a risk model for the domain. The risk model is applied to the document to identify the content segments that may pose a risk of harm to a reader with the user&#39;s expertise level. The document with the visual cues can then be displayed to the user.

BACKGROUND

Vast amounts of documents and website content are available on theInternet, providing a wide variety in the quality and ease ofconsumption of information. When documents and websites contain foreign,complex, and/or unfamiliar language, users may struggle to understandimportant terms or concepts. For instance, users who search for medicalinformation may search for information using keywords which arefamiliar. However, providers of the documents or websites do notevaluate user comprehension levels. Therefore, some users are unable tofully understand the content. In some domains, such as the medicaldomain, this lack of comprehension may lead to a risk of harm to theuser.

SUMMARY

Disclosed herein is a method for providing visual cues in a document forenhanced risk comprehension, and a computer program product and systemas specified in the independent claims. Embodiments of the presentinvention are given in the dependent claims. Embodiments of the presentinvention can be freely combined with each other if they are notmutually exclusive.

According to an embodiment of the present invention, a server captures aset of one or more search terms input by a user and detects a userselection of a document of a plurality of documents returned based onthe search term(s). The server determines a dataset describing the user,where the dataset includes the search term(s), the selected document,and a set of demographic data of the user. The server maps the datasetto a given user group of a plurality of user groups, where the givenuser group is associated with an expertise level for a domain associatedwith the selected document. The server assigns the expertise levelassociated with the given user group to the user and maps the expertiselevel of a given risk model for the domain of a plurality of riskmodels. The server applies one or more visual cues to one or morecontent segments of the selected document based on the given risk model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example computing environment for providing visualcues in a document for enhanced risk comprehension according to someembodiments.

FIG. 2 illustrates a method for providing visual cues in a document forenhanced risk comprehension according to some embodiments.

FIG. 3 illustrates a view of a clinical trials website in an exampleaccording to some embodiments.

FIG. 4 illustrates a view of the search results in an example accordingto some embodiments.

FIG. 5 illustrates a view of contents of the selected document displayedby the browser in an example according to some embodiments.

FIG. 6 illustrates a view of a portion of the document with the visualcues applied to content segments of the document in an example accordingto some embodiments.

FIG. 7 illustrates a view of the document with a risk of understandingscore in an example according to some embodiments.

FIG. 8 illustrates a view of the document with buttons applied to a termin an example according to some embodiments.

FIG. 9 illustrates an example computer system, which may be used forsome embodiments described herein

DETAILED DESCRIPTION

FIG. 1 illustrates an example computing environment for providing visualcues in a document for enhanced risk comprehension according to someembodiments. The computing environment includes a client device 108 witha browser 109 for accessing a website hosted by a web server 106. Aserver 100 with an analytics engine 101 provides an enhanced riskcomprehension service integrated with a website and/or installed on theclient device 108 as part of the browser 109 or a document viewer (notshown) at the client device 108. A “document” in this embodiment iscontent provided by a content provider over a network 107, such as theInternet. In providing the service, the analytics engine 101 uses aplurality of user groups 103, a plurality of risk models 104, and aplurality of user datasets 105, stored in a storage 102, as describedfurther below. The server 100, client device 108, and web server 106communicate over the network 107.

FIG. 2 illustrates a method for providing visual cues in a document forenhanced risk comprehension according to some embodiments. When a userof the client device 108 accesses a website hosted by the web server 106via the browser 109, the user may search for documents of interest byentering a set of one or more search terms, comprising one or morewords, into the website. Assume for example, that the website and/or thebrowser 109 has integrated within it the enhanced risk comprehensionservice provided by the server 100. In one exemplary embodiment,integration occurs client side, as an extension, plugin, or usingActiveX with the browser 109. The integration may further include theuse of tracking cookies. In another exemplary embodiment, theintegration may be implemented as an add-in to a document viewerapplication at the client device 108. In another exemplary embodiment,the integration may be implemented server side as a page fragmentaggregated on the client rendered webpage. In another exemplaryembodiment, the functionality may be implemented server side 100 acrossthe user's session with the web server 106. The analytics engine 101captures the set of search terms input by the user (201). After thewebsite returns a search result containing a plurality of documentsbased on the set of search terms, the analytics engine 101 detects theselection of a document of the plurality of documents by the user (202).The document contains risk information, i.e., information in which alack of understanding of the information may pose a risk of harm to thereader. The analytics engine 101 then determines a dataset describingthe user (203). The dataset may include one or more of: the set ofsearch terms input by the user; the selected document; and a set ofdemographic data for the user. Example demographics for the user mayinclude, but is not limited to, one or more of the following: location;IP address; gender; age; medical history; cognitive capabilities;education level; and job history. In a scenario where demographic datafor the user is not available, the set of demographic data may be null.Information concerning the user or the client device 108 may also begathered from browser data, such as through the use of cookies, andincluded in the dataset. Optionally, a survey may be generated by theserver 100 and sent to the browser 109, and the answers to the surveyare then included in the dataset.

Once the dataset describing the user is determined, the analytics engine101 maps the dataset to a user group of the plurality of user groups103, where the user group is associated with an expertise level for thedomain associated with the selected document (204). The document'sdomain may be determined using an ontology-based document classificationtechnique. The analytics engine 101 then assigns the expertise levelassociated with the user group to the user (205). In an exemplaryembodiment, in mapping the user's dataset to the user group, theanalytics engine 101 compares the user's dataset with parameters for oneor more of the plurality of user groups 103. Each of the plurality ofuser groups 103 is associated with a level of expertise in the domainassociated with the selected document. The plurality of user groups 103may be built through the analysis of the user datasets 105 associatedwith other users from previous searches and improved over time usingcognitive learning algorithms. In an exemplary embodiment, the analyticsengine 101 calculates a similarity score between the user's dataset andone or more of the plurality of user groups 103. When the similarityscore for a given user group exceeds a configurable similarity scorethreshold, the analytics engine 101 maps the user's dataset to this usergroup and assigns the expertise level associated with the user group tothe user. When the similarity score for the given user group does notexceed the similarity score threshold, the process may be repeated forthe next user group in the plurality of user groups 103 until thesimilarity score threshold is exceeded. If the similarity scorethreshold is not exceeded for any of the plurality of user groups 103,then the analytics engine 101 may be configured to assign a defaultexpertise level to the user.

For example, assume that the parameters for a user group includes acombination of a neighborhood (location), an age range, and an averageeducation level. Assume that the user group is associated with an expertlevel for the domain associated with the selected document. Thesimilarity score may be based on a combination of the user's closenessto the location, whether the user is within the age range, and whetherthe user's education level is above or below the average educationlevel. If the similarity score exceeds the configured similarity scorethreshold, then the analytics engine 101 determines that the user'sdataset maps to this user group. The user's dataset may then be added tothe plurality of user groups 103 and stored as part of the user datasets105. In this way, a feedback loop is created such that the accuracy ofthe plurality of user groups 103 are continually improved upon.

In an exemplary embodiment, the analytics engine 101 may have access toan ontology database (not shown), in which each term is associated witha set of domains, and each domain in the set is associated with anexpertise level. The analytics engine 101 compares the set of searchterms input by the user with the terms in the ontology database. Whenone or more matches are found, the analytics engine 101 compares thedomain associated with the selected document with the set of domainsassociated with the matching term. The analytics engine 101 may adjustthe expertise level of the user using the expertise level associatedwith the matching domain. Optionally, when the similarity score,calculated as described above, is not exceeded for any of the pluralityof user groups 103, the expertise level associated with the matchingdomain may be assigned to the user instead of applying a defaultexpertise level.

In an exemplary embodiment, the history of the selected document may beconsidered. For example, the analytics engine 101 may have access to ahistory of document selections by domain experts. When the analyticsengine 101 finds that the selected document has a history of beingselected by experts in the domain, the analytics engine 101 may adjustthe expertise level of the user higher.

In an exemplary embodiment, the referral source may be considered. Forexample, the analytics engine 101 may have access to a referral sourcedatabase (not shown) containing referral sources, each associated withan expertise level for a set of domains. For example, if the domain is‘medical’, a medical journal commonly read by medical experts may beassociated with an expert level for the medical domain, while a laypublication may be associated with a lay level of expertise for themedical domain. When the analytics engine 101 determines that thematching domain for the referral source is associated with an expertlevel, the analytics engine 101 may adjust the expertise level of theuser higher. Similarly, when the matching domain for the referral sourceis associated with a lay level of expertise, the analytics engine 101may keep the expertise level of the user the same or adjust it lower.

Once an expertise level is assigned to the user, the analytics engine101 maps the expertise level to a risk model of the plurality of riskmodels 104 for the domain associated with the document (206). The riskmodel captures the words, phrases, or concepts in the domain in which alack of understanding may pose a risk of harm to readers. In anexemplary embodiment, each risk model of the plurality of risk models104 is associated with a domain. The analytics engine 101 compares thedomain associated with the document with the domain associated with agiven risk model. The expertise level assigned to the user is thenmapped to the given risk model. Based on the risk model, visual cues areapplied to one or more content segments of the selected document, toassist the user's comprehension of the risk information in the selecteddocument (207). Each content segment may contain a word, a phrase, animage, a video, or other method of conveying information in the selecteddocument. In an exemplary embodiment, the risk model is applied to theselected document to identify the content segments that may pose a riskof harm to a reader with the user's expertise level. A visual cue isthen applied to each content segment identified by the analytics engine101. The selected document with the visual cues can then be displayed tothe user. Example visual cues may include the highlighting of text,replacement of text with other or simpler forms of text, applying asemantic tag to the content segment with a potential discussion forum orpeople to contact for further information, and the highlighting of otherrelevant documents. In an exemplary embodiment, the visual cues mayinclude a calculated overall risk of understanding score. The risk ofunderstanding score represents an overall level of harm to a reader withthe user's expertise level if there is a lack of understanding of thecontent segments in the selected document. In an exemplary embodiment,the calculation of the risk of understanding score begins with abaseline score using a certain set of user demographic data, and therisk of understanding score is calculated relative to this baselinescore. In other exemplary embodiments, the risk of understanding scoreis calculated based on a set of surveyed demographic data whichindicates a user's risk of understanding, based on an expert-determinedrisk model, or using natural language associations between the user'sset of search terms and a risk level.

In an exemplary embodiment, once the selected document with the visualcues are displayed by the browser 109, the analytics engine 101 maytrack the user's interaction with the selected document. For example,the dwell time on a given content segment, such as based on eye gaze ormouse hover/movement, may be considered. A long dwell time on a givencontent segment may indicate a lack of understanding by the user. Athreshold dwell time may be configured such that when the dwell timeexceeds the threshold, the analytics engine 101 responds by promptingthe user to confirm his or her understanding or respond by displayingfurther resources for the user to gain further understanding. The dwelltime can then be added to the user's dataset. Optionally, the analyticsengine 101 can update the mapping of the user's dataset to a user groupof the plurality of user groups 103 based on the dwell time, using theprocess described above, and change the application of visual cuesaccordingly.

FIGS. 3-8 illustrates an example of applying visual cues to a documentaccording to some embodiments. Assume in this example that Alice, theuser of the client device 108, via the browser 109, is in search of aclinical trial for a family member who has suffered a heart attack.Assume also that Alice is a user without medical education or training.Alice accesses the website at “ClinicalTrials.gov”, which is integratedwith an embodiment of the present invention and is associated with the“medical” domain. The analytics engine 101 begins monitoring Alice'ssession with the website as determined by a session identifier, such asa cookie or a stateless server-side identification. FIG. 3 illustratesan example view of the clinical trials website 300. Assume that thewebsite 300 includes a search field 301, into which Alice enters “heartattack” as the search terms. Referring also to FIG. 2, the analyticsengine 101 captures the search terms, “heart attack”, inputted by Alice(201) and stores “heart attack” as part of Alice's dataset. The website300 then displays a plurality of documents returned as the searchresults for “heart attack”. FIG. 4 illustrates an example view of thesearch results 400. Assume that Alice selects document 401 from thesearch results 400. The document 401 includes risk informationconcerning heart attacks. The analytics engine 101 detects Alice'sselection of the document 401 (202) and stores the selection as part ofAlice's dataset. The document 401 is then displayed via the browser 109.FIG. 5 illustrates an example view of the contents of the selecteddocument 401 displayed by the browser 109.

The analytics engine 101 then executes a detection routine to determineAlice's demographic data and stores the demographics data as part ofAlice's dataset (203). Assume in this example that the dataset includes:IP address=192.168.0.1 (IP address of the client device 108); searchterm=“heart attack”; and document=“doc 2” (identifier for the document401). Assume that the analytics engine 101 maps Alice's dataset to auser group associated with a “lay user” expertise level for the medicaldomain (204). The analytics engine 101 thus assigns the “lay user”expertise level to Alice (205). The analytics engine 101 then maps the“lay user” expertise level to a risk model for the medical domain (206).

Based on the mapping of the “lay user” expertise level to the riskmodel, the analytics engine 101 applies visual cues to one or morecontent segments of the document 401 (207), and the document 401 withthe visual cues are displayed via the browser 109 or a document viewerat the client device 108. FIG. 6 illustrates an example view of aportion of the document 401 with the visual cues applied to contentsegments of the document 401. In this example, per the risk model, theterms, “platelet phenotype” 601, “platelet genetic composition” 602, and“myocardial infarction” 603 are identified to have a high risk of harmfor a lay user expertise level. In this example, these terms 601, 602,602 are highlighted, however, other types or combinations of visual cuesmay be used instead. For example, “myocardial infarction” 603 may beidentified by the risk model as having a higher risk of harm than“platelet phenotype” 601 and “platelet genetic composition” 602, andthus, “myocardial infarction” 603 can be highlighted differently.

In an exemplary embodiment, the visual cues include a calculated overallrisk of understanding score. The risk of understanding score representsan overall level of harm to a reader with the user's expertise level ifthere is a lack of understanding of the content segments in the selecteddocument 401. FIG. 7 illustrates an example view of the document 401with a risk of understanding score 701. Assume that the risk ofunderstanding score for the document 401 is calculated to be ‘80’ forAlice, indicating a high risk of harm for Alice as a lay user if therisk information in the document 401 is not properly understood byAlice.

As Alice reads the document 401, the analytics engine 101 continues tomonitor Alice's interactions with the website 300 and uses the datacollected to continue building Alice's dataset. For example, theanalytics engine 101 records Alice's dwell time with highlighted contentsegment, using eye gaze, points, or interpolated dwell time throughcursor movement. Optionally, a visual cue may be applied to the document401 to prompt Alice to confirm her understanding of the highlightedcontent segment once the dwell time has passed. FIG. 8 illustrates anexample view of the document 401 with a ‘yes’ button 801 and a ‘no’button 802 applied to the term “platelet phenotype”. Alice may thenselect the appropriate button to indicate whether she understands theterm “platelet phenotype”. The analytics engine 101 records Alice'sresponse and stores the response as part of Alice's dataset. Optionally,Alice's expertise level may be recalculated, and the visual cues in thedocument 401 may be updated accordingly.

FIG. 9 illustrates an example computer system, which may be used forsome embodiments described herein. The computer system 900 isoperationally coupled to a processor or processing units 906, a memory901, and a bus 909 that couples various system components, including thememory 901 to the processor 906. The bus 909 represents one or more ofany of several types of bus structure, including a memory bus or memorycontroller, a peripheral bus, an accelerated graphics port, and aprocessor or local bus using any of a variety of bus architectures. Thememory 901 may include computer readable media in the form of volatilememory, such as random access memory (RAM) 902 or cache memory 903, ornon-volatile storage media 904. The memory 901 may include at least oneprogram product having a set of at least one program code module 9 thatare configured to carry out the functions of embodiment of the presentinvention when executed by the processor 906. The computer system 900may also communicate with one or more external devices 911, such as adisplay 910, via 110 interfaces 907. The computer system 900 maycommunicate with one or more networks via network adapter 908.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for providing visual cues in a documentby a server, comprising: capturing, by the server, a set of one or moresearch terms input by a user; after a display of a search resultcomprising a plurality of documents based on the set of one or moresearch terms, detecting, by the server, a user selection of a documentof the plurality of documents in the search result, the selecteddocument comprising a plurality of content segments; determining, by theserver, a dataset describing the user, the dataset comprising the set ofone or more search terms, the selected document, and a set ofdemographic data of the user; mapping, by the server, the dataset to afirst given user group of a plurality of user groups, the first givenuser group being associated with a first expertise level for a domainassociated with the selected document; assigning, by the server, thefirst expertise level associated with the first given user group to theuser; mapping, by the server, the first expertise level to a first givenrisk model for the domain of a plurality of risk models; identifying, bythe server, a first set of content segments of the plurality of contentsegments that would pose a risk of harm to the reader with the firstexpertise level assigned to the user based on the first given riskmodel; applying, by the server, a first set of visual cues to the firstset of content segments comprised in the selected document; displaying,by the server, the selected document with the first set of visual cuesapplied to the first set of content segments; measuring, by the server,a dwell time of the user on a given content segment of the first set ofcontent segments displayed with a given visual cue of the first set ofvisual cues; and updating, by the server, the display of the selecteddocument based on the dwell time, comprising: updating, by the server,the dataset describing the user by adding the dwell time to the dataset;mapping, by the server, the updated dataset to a second given user groupof the plurality of user groups, the second given user group beingassociated with a second expertise level for the domain associated withthe selected document; assigning, by the server, the second expertiselevel associated with the second given user group to the user; mapping,by the server, the second expertise level to a second given risk modelfor the domain of the plurality of risk models; identifying, by theserver, a second set of content segments of the plurality of contentsegments that would pose a risk of harm to the reader with the secondexpertise level assigned to the user based on the second given riskmodel; applying, by the server, a second set of visual cues to thesecond set of content segments comprised in the selected document; andupdating, by the server, the display of the selected document with thesecond set of visual cues applied to the second set of content segments.2. The method of claim 1, wherein the mapping of the dataset to thefirst given user group comprises: determining, by the server, the domainassociated with the selected document; comparing, by the server, thedataset with parameters for one or more of the plurality of user groups,wherein each of the plurality of user groups is associated with a levelof expertise in the domain; calculating, by the server, a similarityscore based on the comparing with the first given user group of theplurality of user groups; and when the similarity score exceeds asimilarity score threshold, assigning, by the server, the first level ofexpertise associated with the first given user group to the user.
 3. Themethod of claim 1, wherein the assigning of the first expertise level ofthe first given user group to the user comprises: comparing, by theserver, the set of one or more search terms with a plurality of terms inan ontology database, wherein each term in the ontology database isassociated with a set of domains, wherein each domain in the set isassociated with a third expertise level; when the set of one or moresearch terms matches a given term in the ontology database, comparing,by the server, the domain associated with the selected document with theset of domains associated with the given term; and adjusting, by theserver, the first expertise level assigned to the user according to thethird expertise level associated with the matching domain.
 4. The methodof claim 1, wherein the dataset further comprises a referral source,wherein the assigning of the first expertise level of the first givenuser group to the user comprises: comparing, by the server, the referralsource with a plurality of referral sources in a referral sourcedatabase, wherein each referral source in the referral source databaseis associated with a set of domains, wherein each domain in the set isassociated with a third expertise level; when the referral sourcematches a given referral source in the referral source database,comparing, by the server, the domain associated with the selecteddocument with the set of domains associated with the given referralsource; and adjusting, by the server, the first expertise level assignedto the user according to the third expertise level associated with thematching domain.
 5. The method of claim 1, wherein the identifying ofthe first set of content segments comprises: calculating, by the server,an overall risk of understanding score representing an overall level ofrisk of harm to a reader with the first expertise level assigned to theuser with lack of understanding of the first set of content segments inthe selected document.
 6. A computer program product for providingvisual cues in a document, the computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a processor to causethe processor to: capture a set of one or more search terms input by auser; after a display of a search result comprising a plurality ofdocuments based on the set of one or more search terms, detect a userselection of a document of the plurality of documents in the searchresult, the selected document comprising a plurality of contentsegments; determine a dataset describing the user, the datasetcomprising the set of one or more search terms, the selected document,and a set of demographic data of the user; map the dataset to a firstgiven user group of a plurality of user groups, the first given usergroup being associated with a first expertise level for a domainassociated with the selected document; assign the first expertise levelassociated with the first given user group to the user; map the firstexpertise level to a first given risk model for the domain of aplurality of risk models for the domain; identify a first set of contentsegments of the plurality of content segments that would pose a risk ofharm to the reader with the first expertise level assigned to the userbased on the first given risk model; apply a first set of visual cues tothe first set of content segments comprised in the selected document;display the selected document with the first set of visual cues appliedto the first set of content segments; measure a dwell time of the useron a given content segment of the first set of content segmentsdisplayed with a given visual cue of the first set of visual cues; andupdate the display of the selected document based on the dwell time,comprising: update the dataset describing the user by adding the dwelltime to the dataset; map the updated dataset to a second given usergroup of the plurality of user groups, the second given user group beingassociated with a second expertise level for the domain associated withthe selected document; assign the second expertise level associated withthe second given user group to the user; map the second expertise levelto a second given risk model for the domain of the plurality of riskmodels; identify a second set of content segments of the plurality ofcontent segments that would pose a risk of harm to the reader with thesecond expertise level assigned to the user based on the second givenrisk model; apply a second set of visual cues to the second set ofcontent segments comprised in the selected document; and update thedisplay of the selected document with the second set of visual cuesapplied to the second set of content segments.
 7. The computer programproduct of claim 6, wherein the mapping of the dataset to the firstgiven user group comprises: determine the domain associated with theselected document; compare the dataset with parameters for one or moreof the plurality of user groups, wherein each of the plurality of usergroups is associated with a level of expertise in the domain; calculatea similarity score based on the comparing with the given user group ofthe plurality of user groups; and when the similarity score exceeds asimilarity score threshold, assign the first level of expertiseassociated with the first given user group to the user.
 8. The computerprogram product of claim 6, wherein the assigning of the first expertiselevel of the first given user group to the user comprises: compare theset of one or more search terms with a plurality of terms in an ontologydatabase, wherein each term in the ontology database is associated witha set of domains, wherein each domain in the set is associated with athird expertise level; when the set of one or more search terms matchesa given term in the ontology database, compare the domain associatedwith the selected document with the set of domains associated with thegiven term; and adjust the expertise level assigned to the useraccording to the third expertise level associated with the matchingdomain.
 9. The computer program product of claim 6, wherein the datasetfurther comprises a referral source, wherein the assigning of the firstexpertise level of the first given user group to the user comprises:compare the referral source with a plurality of referral sources in areferral source database, wherein each referral source in the referralsource database is associated with a set of domains, wherein each domainin the set is associated with a third expertise level; when the referralsource matches a given referral source in the referral source database,compare the domain associated with the selected document with the set ofdomains associated with the given referral source; and adjust the firstexpertise level assigned to the user according to the third expertiselevel associated with the matching domain.
 10. The computer programproduct of claim 6, wherein the identifying of the first set of contentsegments comprises: calculate an overall risk of understanding scorerepresenting an overall level of risk of harm to a reader with the firstexpertise level assigned to the user with lack of understanding of thefirst set of content segments in the selected document.
 11. A systemcomprising: a processor; and a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by the processor to cause the processor to: capture a set ofone or more search terms input by a user; after a display of a searchresult comprising a plurality of documents based on the set of one ormore search terms, detect a user selection of a document of theplurality of documents in the search results, the selected documentcomprising a plurality of content segments; determine a datasetdescribing the user, the dataset comprising the set of one or moresearch terms, the selected document, and a set of demographic data ofthe user; map the dataset to a first given user group of a plurality ofuser groups, the first given user group being associated with a firstexpertise level for a domain associated with the selected document;assign the first expertise level associated with the first given usergroup to the user; map the first expertise level to a first given riskmodel for the domain of a plurality of risk models for the domain;identify a first set of content segments of the plurality of contentsegments that would pose a risk of harm to the reader with the firstexpertise level assigned to the user based on the first given riskmodel; apply a first set of visual cues to the first set of contentsegments comprised in the selected document; display the selecteddocument with the first set of visual cues applied to the first set ofcontent segments; measure a dwell time of the user on a given contentsegment of the first set of content segments displayed with a givenvisual cue of the first set of visual cues; and update the display ofthe selected document based on the dwell time, comprising: update thedataset describing the user by adding the dwell time to the dataset; mapthe updated dataset to a second given user group of the plurality ofuser groups, the second given user group being associated with a secondexpertise level for the domain associated with the selected document;assign the second expertise level associated with the second given usergroup to the user; map the second expertise level to a second given riskmodel for the domain of the plurality of risk models; identify a secondset of content segments of the plurality of content segments that wouldpose a risk of harm to the reader with the second expertise levelassigned to the user based on the second given risk model; apply asecond set of visual cues to the second set of content segmentscomprised in the selected document; and update the display of theselected document with the second set of visual cues applied to thesecond set of content segments.
 12. The system of claim 11, wherein themapping of the dataset to the first given user group comprises:determine the domain associated with the selected document; compare thedataset with parameters for one or more of the plurality of user groups,wherein each of the plurality of user groups is associated with a levelof expertise in the domain; calculate a similarity score based on thecomparing with the given user group of the plurality of user groups; andwhen the similarity score exceeds a similarity score threshold, assignthe first level of expertise associated with the first given user groupto the user.
 13. The system of claim 11, wherein the assigning of thefirst expertise level of the first given user group to the usercomprises: compare the set of one or more search terms with a pluralityof terms in an ontology database, wherein each term in the ontologydatabase is associated with a set of domains, wherein each domain in theset is associated with a third expertise level; when the set of one ormore search terms matches a given term in the ontology database, comparethe domain associated with the selected document with the set of domainsassociated with the given term; and adjust the expertise level assignedto the user according to the third expertise level associated with thematching domain.
 14. The system of claim 11, wherein the dataset furthercomprises a referral source, wherein the assigning of the firstexpertise level of the first given user group to the user comprises:compare the referral source with a plurality of referral sources in areferral source database, wherein each referral source in the referralsource database is associated with a set of domains, wherein each domainin the set is associated with a third expertise level; when the referralsource matches a given referral source in the referral source database,compare the domain associated with the selected document with the set ofdomains associated with the given referral source; and adjust the firstexpertise level assigned to the user according to the third expertiselevel associated with the matching domain.